diff --git a/website/usage/_linguistic-features/_dependency-parse.jade b/website/usage/_linguistic-features/_dependency-parse.jade
index 188b7b8f3..d8d7cbce1 100644
--- a/website/usage/_linguistic-features/_dependency-parse.jade
+++ b/website/usage/_linguistic-features/_dependency-parse.jade
@@ -65,9 +65,9 @@ p
     - var style = [0, 1, 0, 1, 0]
     +annotation-row(["Autonomous", "amod", "cars", "NOUN", ""], style)
     +annotation-row(["cars", "nsubj", "shift", "VERB", "Autonomous"], style)
-    +annotation-row(["shift", "ROOT", "shift", "VERB", "cars, liability"], style)
+    +annotation-row(["shift", "ROOT", "shift", "VERB", "cars, liability, toward"], style)
     +annotation-row(["insurance", "compound", "liability", "NOUN", ""], style)
-    +annotation-row(["liability", "dobj", "shift", "VERB", "insurance, toward"], style)
+    +annotation-row(["liability", "dobj", "shift", "VERB", "insurance"], style)
     +annotation-row(["toward", "prep", "liability", "NOUN", "manufacturers"], style)
     +annotation-row(["manufacturers", "pobj", "toward", "ADP", ""], style)
 
diff --git a/website/usage/_linguistic-features/_named-entities.jade b/website/usage/_linguistic-features/_named-entities.jade
index 9e55ba84e..0f32d1da3 100644
--- a/website/usage/_linguistic-features/_named-entities.jade
+++ b/website/usage/_linguistic-features/_named-entities.jade
@@ -80,7 +80,7 @@ p
     doc.ents = [netflix_ent]
 
     ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
-    assert ents = [(u'Netflix', 0, 7, u'ORG')]
+    assert ents == [(u'Netflix', 0, 7, u'ORG')]
 
 p
     |  Keep in mind that you need to create a #[code Span] with the start and
diff --git a/website/usage/_linguistic-features/_tokenization.jade b/website/usage/_linguistic-features/_tokenization.jade
index f149556ce..2cd3a13de 100644
--- a/website/usage/_linguistic-features/_tokenization.jade
+++ b/website/usage/_linguistic-features/_tokenization.jade
@@ -54,7 +54,7 @@ p
 
 +code.
     import spacy
-    from spacy.symbols import ORTH, LEMMA, POS
+    from spacy.symbols import ORTH, LEMMA, POS, TAG
 
     nlp = spacy.load('en')
     doc = nlp(u'gimme that') # phrase to tokenize